A genetic algorithm based decision support system for forecasting security prices in stock index

Abstract

Abstract Recent studies in finance argue that technical analysis has the ability to predict stock prices. Though a variety of systems are used for market assessment and timing, past research has shown very little interest in optimizing the parameters of these systems. Genetic Algorithms (GA) are a soft computing based optimization procedure that optimizes a rule or parameters of a rule where search space is very large and it is not practically possible to test each and every parameter combination due to limited processing power and time. In this research we have used a GA based approach to optimize parameters of a pre-defined rule set that predicts the next-day’s stock price. Results obtained from our experiments are promising and encouraging enough to lead us to believe that Genetic Algorithm (GA) is an appropriate way of addressing these types of NP hard problems.